Changing Graph Use in Corporate Annual Reports: A Time‐Series Analysis
Bibliographic record
Abstract
Abstract Graphs in corporate annual reports form part of a powerfully designed annual report package that offers considerable potential for “impression management.” The primary purpose of this paper is to determine whether graph use depends on corporate performance. Time‐series analysis, not previously used in the financial graphs literature, allows discretionary changes in graph use by companies to be identified and related to changes in individual companies' corporate performance over time. Based on the prior financial graphs and accounting choice literature, we develop two hypotheses that relate changes in graph use to changes in corporate performance. These hypotheses focus on the aggregate and individual company levels. We base our analysis on the corporate annual reports of 137 top UK companies that were in continued existence during the five‐year period from 1988 to 1992. At both the aggregate and individual company levels, we find the decision to use key financial variable (KFV) graphs, the primary graphical choice, to be associated positively with corporate performance measures. This finding is consistent with the manipulation hypothesis ‐ that is, that financial graphs in corporate annual reports are used to “manage” favorably the reader's impression of company performance, and hence that there is a reporting bias.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".